RUI New Generation of AI-Augmented Quantum Monte Carlo Libraries to Guide the Search for Exotic Superfluid Phases in Cold Atoms

RUI 新一代人工智能增强量子蒙特卡罗库指导冷原子中奇异超流体相的搜索

• 批准号: 2207048
• 负责人: Ettore Vitali
• 金额: $ 15万
• 依托单位: California State University-Fresno Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2207048&HistoricalAwards=false
• 关键词: RUI; New; Generation; AI; Augmented

This project will interface well-established state-of-the-art numerical techniques with innovative artificial intelligence tools to compute equilibrium and dynamical properties of strongly correlated Fermi superfluids. In the very active field of ultracold atoms, the results of this project will guide experimental searches for exotic superfluid phases, such as non-trivial superfluid states of matter that rely on unconventional pairing mechanisms or the existence of fascinating topological superfluids. The new insights that will come from this project may have a deep impact in physics, even beyond the field of cold atoms, and may pave the way for a deeper understanding of superfluid states in unconventional superconductors and in nuclear matter. Novel data analysis tools and visualization techniques will be developed to capture the most important physical mechanisms. The PI plans to dedicate substantial effort to make the methods and topics accessible to as many people as possible, and to address novel ways to teach quantum mechanics. Students will be able to run virtual experiments and to explore new phases of matter and will have a unique opportunity to learn how to become the scientists of the future.A new generation of auxiliary-field quantum Monte Carlo techniques will be proposed: artificial intelligence tools will guide a self-consistent procedure to minimize the bias due to the approximations underlying the technique. This will be achieved through the optimization of the trial wave function within a feedback process which learns from the quantum Monte Carlo data. This project is expected to generate accurate non-perturbative data for Fermi superfluids. The study will address both equilibrium properties, like pairing, density and spin correlations, and dynamical properties, like spectral functions and dynamical structure factors, which give access to the low-energy excitations of the systems. The accurate numerical data will provide guidance to the experiments in the major challenge of detecting new exotic phases in cold atoms, with particular focus on complex intertwined orders in spin-polarized systems when spin-orbit coupling is present. Substantial advances in the fundamental understanding of the physical mechanisms underlying such superfluid phases are also expected. The formation of Cooper pairs with finite momentum in spin-polarized systems will be addressed, the interplay with density and spin order will be studied and the role of spin-orbit coupling will be investigated, in connection with the possibility of observing non-trivial topological properties. The study of the manifold of excited states of a system through the calculation of dynamical correlations will allow the PI to directly compare the predictions with spectroscopy and scattering experiments and to compute the dispersion of collective modes, which is crucial for the experimental detection of exotic phases. In addition to guiding experimental searches, the results will also serve as valuable benchmarks for many-body theories and computational methods for strongly correlated systems, where simple perturbative approaches are doomed to fail. Finally, the efforts that the PI plans to dedicate to the development of novel visualization tools to capture the key physical mechanisms are expected to create the best environment to train the next generation of students, teachers and researchers in quantum mechanics, starting from their undergraduate studies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目将使用创新的人工智能工具将良好的最新数值技术接口，以计算强烈相关的费米超级流体的平衡和动力学特性。在超电原子的非常活跃的领域，该项目的结果将指导对异国情调超流体相的实验搜索，例如依赖非常规配对机制或引人入胜的拓扑超级流体的物质的非平凡的超流体状态。该项目将带来的新见解可能会对物理学，甚至在冷原子领域中产生深远的影响，并且可能为在非常规超导体和核物质中更深入地了解超流体状态铺平道路。新型数据分析工具和可视化技术将开发以捕获最重要的物理机制。 PI计划致力于使尽可能多的人可以访问的方法和主题，并探讨教授量子力学的新方法。学生将能够进行虚拟实验并探索物质的新阶段，并有一个独特的机会来学习如何成为未来的科学家。将提出新一代的辅助场量子蒙特卡洛技术：人工智能工具将提出：人工智能工具将指导由于该技术而导致的近似偏见，以最大程度地减少偏见。这将通过在反馈过程中优化试验波函数来实现，该反馈过程从量子蒙特卡洛数据中学习。该项目有望为费米超级流体生成准确的非扰动数据。该研究将涉及均衡性能，例如配对，密度和自旋相关性，以及动力学特性，例如光谱函数和动态结构因子，它们可以访问系统的低能激发。准确的数值数据将为检测冷原子中新的外来阶段的主要挑战的实验提供指导，当存在自旋轨道耦合时，特别关注自旋极化系统中的复杂相互交织的顺序。预计对这种超流体阶段的物理机制的基本理解的基本理解也有很大的进步。将解决与自旋偏振系统中有限动量的库珀对形成，将研究与密度和自旋顺序的相互作用，并研究自旋轨道耦合的作用，与观察非客气拓扑特性的可能性有关。通过计算动力相关性来对系统激发态的流形的研究将使PI可以将预测直接与光谱和散射实验进行比较，并计算集体模式的分散体，这对于外来相的实验检测至关重要。除了指导实验搜索外，结果还将作为密切相关系统的多体理论和计算方法的有价值的基准，在这些基准中，简单的扰动方法注定要失败。最后，PI计划致力于开发新颖的可视化工具来捕捉关键的物理机制，以创造最佳的环境，以训练下一代的量子力学的学生，老师和研究人员，从他们的本科研究开始。这项奖项反映了NSF的法定任务，并通过使用基础的智力效果进行评估，并通过评估范围来进行评估。